Introduction

maSigPro is a R package that initially was developed for the analysis of time series of multiple conditions. maSigPro follows a two steps regression strategy to find variable with significant temporal changes and significant differences between experimental conditions The method defines a general regression model for the data where the experimental conditions are identified by dummy variables.

  • The procedure first adjusts this global model by the least-squared technique to identify variables changing over time and selects significant ones applying false discovery rate control procedures.

  • Secondly, stepwise regression is applied as a variable selection strategy to study differences between experimental conditions and to find statistically significant different profiles. The coefficients obtained in this second regression model will be useful to cluster together significant variables with similar patterns over time.

 

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1 Description

1.1 Project

Oxytocin reduces caloric intake in proteomics analysis

1.2 Analysis

maSigPro analysis of 4 obesity + treatment conditions

2 Result

2.1 Clustering

maSigPro identified 11 clusters of 283 variables in total.

Figure 1. Group averages of all genes in each cluster. Color corresponds to measurements (red = high).

Click to view full table of group means.

Figure 2. Inter-correlation between the means of all clusters. Similar clusters have higher correlation coefficient.

Figure 3. Cluster profiles.

2.2 Post-hoc analysis

Post-hoc analysis of the clusters by comparing conditions.

  • The total deviation from the beginning points at each time point.
  • The deviation between the conditions at each time point.

Table 1 The post-hoc analysis of clusters shown in Figure 3. The between statistics represent the maximal deviation (when and how much) between a time point and the beginning time point, and the within statistics represent the maximal deviation (when and how much) of the conditions from each other within the same time point.

Cluster Size Between_When Between_Dev Between_P Within_When Within_Dev Within_P
Cluster_1 15 T120 1.972 2.40e-03 T120 0.5566 1.02e-02
Cluster_2 70 T120 2.700 1.59e-02 T150 0.8227 1.24e-02
Cluster_3 19 T30 2.452 2.96e-04 T60 0.7509 9.23e-05
Cluster_4 20 T60 4.324 1.24e-10 T30 0.3483 5.33e-02
Cluster_5 9 T120 5.128 1.16e-10 T30 0.3888 7.58e-02
Cluster_6 7 T90 2.826 7.34e-06 T15 0.7261 7.06e-04
Cluster_7 68 T120 2.289 3.56e-02 T150 0.6380 4.43e-02
Cluster_8 19 T90 2.341 8.22e-03 T150 0.6411 1.34e-02
Cluster_9 21 T120 2.644 1.09e-02 T150 0.8561 9.10e-03
Cluster_10 23 T120 2.563 5.96e-03 T60 0.7415 8.62e-03
Cluster_11 12 T120 1.840 2.86e-02 T60 0.6601 2.04e-02

Figure 4. The left panel shows when and how much the maximal deviation happens for each cluster between the beginning time point and all the other time points. The right panel shows when and how much the maximal deviation between conditions happens within each time point.

2.3 Over-representation analysis

If the variables can be classified into pre-defined sets, such as genes into Gene Ontology categories, over-representation analysis can be run to test the enrichment of sets in each cluster.

Table 2 Numbers of pre-defined variable sets significantly enriched in each cluster. Sets were split based on their sources. Click on each number to see list of the variable sets.

BioSystems KEGG MSigDb PubTator
Cluster_1 76 10 59 27
Cluster_2 118 15 102 5
Cluster_3 93 7 96 34
Cluster_4 145 7 98 92
Cluster_5 66 1 122 39
Cluster_6 70 0 44 12
Cluster_7 153 6 193 17
Cluster_8 34 6 80 69
Cluster_9 88 3 172 28
Cluster_10 65 11 123 72
Cluster_11 40 1 45 26

3 Appendix

Check out the RoCA home page for more information.

3.1 Reproduce this report

To reproduce this report:

  1. Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory

  2. To generate a new report using your own input data and parameter, edit the following items in the YAML file:

    • output : where you want to put the output files
    • home : the URL if you have a home page for your project
    • analyst : your name
    • description : background information about your project, analysis, etc.
    • input : where are your input data, read instruction for preparing them
    • parameter : parameters for this analysis; read instruction about how to prepare input data

  3. Run the code below within R Console or RStudio, preferablly with a new R session:

if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }

CreateReport(filename.yaml);  # filename.yaml is the YAML file you just downloaded and edited

If there is no complaint, go to the output folder and open the index.html file to view report.

3.2 Session information

## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
##  [1] tcltk     stats4    parallel  stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] gplots_3.0.1         maSigPro_1.42.0      DynDoc_1.48.0       
##  [4] widgetTools_1.48.0   MASS_7.3-45          Biobase_2.30.0      
##  [7] GenomicRanges_1.22.4 GenomeInfoDb_1.6.3   IRanges_2.4.8       
## [10] S4Vectors_0.8.11     BiocGenerics_0.16.1  htmlwidgets_0.9     
## [13] DT_0.2               yaml_2.1.13          rmarkdown_1.3       
## [16] knitr_1.17           RoCA_0.0.0.9000      awsomics_0.0.0.9000 
## [19] RCurl_1.95-4.8       bitops_1.0-6         devtools_1.13.4     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.13       highr_0.6          XVector_0.10.0    
##  [4] tkWidgets_1.48.0   tools_3.2.2        zlibbioc_1.16.0   
##  [7] digest_0.6.12      jsonlite_1.0       evaluate_0.10.1   
## [10] memoise_1.1.0      withr_2.1.0        stringr_1.2.0     
## [13] gtools_3.5.0       caTools_1.17.1     rprojroot_1.2     
## [16] limma_3.26.9       gdata_2.17.0       magrittr_1.5      
## [19] backports_1.1.1    htmltools_0.3.6    KernSmooth_2.23-15
## [22] stringi_1.1.1      Mfuzz_2.30.0

END OF DOCUMENT